Remove static pretrained maps from the library's internals (#29112)
* [test_all] Remove static pretrained maps from the library's internals * Deprecate archive maps instead of removing them * Revert init changes * [test_all] Deprecate instead of removing * [test_all] PVT v2 support * [test_all] Tests should all pass * [test_all] Style * Address review comments * Update src/transformers/models/deprecated/_archive_maps.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/deprecated/_archive_maps.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * [test_all] trigger tests * [test_all] LLAVA * [test_all] Bad rebase --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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@@ -32,7 +32,6 @@ if is_torch_available():
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import torch
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from transformers import (
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GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
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GPT2DoubleHeadsModel,
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GPT2ForQuestionAnswering,
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GPT2ForSequenceClassification,
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@@ -701,9 +700,9 @@ class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
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@slow
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def test_model_from_pretrained(self):
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for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = GPT2Model.from_pretrained(model_name)
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self.assertIsNotNone(model)
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model_name = "openai-community/gpt2"
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model = GPT2Model.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_torch
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@@ -31,7 +31,6 @@ if is_tf_available():
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from transformers import GPT2Tokenizer
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from transformers.models.gpt2.modeling_tf_gpt2 import (
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TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
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TFGPT2DoubleHeadsModel,
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TFGPT2ForSequenceClassification,
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TFGPT2LMHeadModel,
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@@ -422,9 +421,9 @@ class TFGPT2ModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTester
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@slow
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def test_model_from_pretrained(self):
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for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFGPT2Model.from_pretrained(model_name)
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self.assertIsNotNone(model)
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model_name = "openai-community/gpt2"
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model = TFGPT2Model.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# overwrite from common since ONNX runtime optimization doesn't work with tf.gather() when the argument
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# `batch_dims` > 0"
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